Sage Journals: Discover world-class research

Abstract

Background

The novel advanced modeled iterative reconstruction (ADMIRE) algorithm in ultra-high-resolution (UHR) computed tomography (CT) of the temporal bone has not yet been systematically evaluated.

Purpose

To assess the potential of ADMIRE in temporal bone UHR CT.

Material and Methods

Forty-four patients who underwent UHR CT of the temporal bone using z-axis UHR protocol were retrospectively selected for analysis. Images were reconstructed using filtered back projection (FBP) and ADMIRE with multiple strength levels. Regions of interest were drawn in the posterior fossa and petrous bone. The average density (in Hounsfield units [HU]) and the image noise (standard deviation of density values) were extracted. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were then calculated. Additionally, a subjective qualitative analysis was performed using a five-point Likert scale. The potential dose reduction was extrapolated from objective analysis and confirmed in an additional phantom study.

Results

The image noise was significantly lower, and the SNR and CNR were significantly higher in ADMIRE reconstructions levels A2–A5 than in FBP (P < 0.001, respectively). Subjective image quality was significantly higher in ADMIRE levels A2–A5 than in FBP (P < 0.001) and it was rated best in ADMIRE level A3. Confirmed by the results from the phantom study, a dose reduction of at least 40% was feasible while maintaining image quality.

Conclusions

The ADMIRE reconstruction algorithm significantly improves image quality and reduces noise on temporal bone UHR CT scans. Thus, it allows for substantial dose reduction.

Keywords

Computed tomography CT temporal bone iterative reconstruction advanced modeled iterative reconstruction ADMIRE

Get full access to this article

View all access options for this article.

References

Casselman

Offeciers

Foer

B de

, et al. CT and MR imaging of congential abnormalities of the inner ear and internal auditory canal. Eur J Radiol 2001; 40:94–104.

Koch

Hamilton

Hudgins

et al. , editors. Diagnostic Imaging: Head and Neck. Philadelphia, PA: Elsevier; 2017.

Young

Ryan

Young

NM.

Preoperative imaging of sensorineural hearing loss in pediatric candidates for cochlear implantation.

Radiographics 2014; 34:E133–49.

Fishman

AJ.

Imaging and anatomy for cochlear implants.

Otolaryngol Clin North Am 2012; 45:1–24.

Rutherford

Lerer

Schoem

, et al. Evaluation of pediatric sensorineural hearing loss: A survey of pediatric otolaryngologists. Ann Otol Rhinol Laryngol 2011; 120:674–681.

NIH - National Cancer Institute. Radiation Risks and Pediatric Computed Tomography: A Guide for Health Care Providers. Available at: https://www.cancer.gov/about-cancer/causes-prevention/risk/radiation/pediatric-ct-scans (last accessed 12 Sep 2017).

Nauer

Rieke

Zubler

, et al. Low-dose temporal bone CT in infants and young children: effective dose and image quality. Am J Neuroradiol 2011; 32:1375–1380.

Solomon

Mileto

Ramirez-Giraldo

, et al. Diagnostic performance of an advanced modeled iterative reconstruction algorithm for low-contrast detectability with a third-generation dual-source multidetector CT scanner: potential for radiation dose reduction in a multireader study. Radiology 2015; 275:735–745.

Beister

Kolditz

Kalender

WA.

Iterative reconstruction methods in X-ray CT.

Phys Med 2012; 28:94–108.

10.

Thibault

J-B

Sauer

Bouman

, et al. A three-dimensional statistical approach to improved image quality for multislice helical CT. Med Phys 2007; 34:4526–4544.

11.

Pickhardt

Lubner

Kim

, et al. Abdominal CT with model-based iterative reconstruction (MBIR): initial results of a prospective trial comparing ultralow-dose with standard-dose imaging. Am J Roentgenol 2012; 199:1266–1274.

12.

Elm

E von

Altman

Egger

, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. J Clin Epidemiol 2008; 61:344–349.

13.

Flohr

Stierstorfer

Suss

, et al. Novel ultrahigh resolution data acquisition and image reconstruction for multi-detector row CT. Med Phys 2007; 34:1712–1723.

14.

Siemens Healthcare. ADMIRE – Advanced Modeled Iterative Reconstruction. Available at: https://www.healthcare.siemens.de/computed-tomography/technologies-innovations/admire (last accessed 13 Sep 2017).

15.

Wang

, et al. Improving depiction of temporal bone anatomy with low-radiation dose CT by an integrated circuit detector in pediatric patients: a preliminary study. Medicine 2014; 93:e325.

16.

Niu

Olszewski

Zhang

, et al. Experimental study and optimization of scan parameters that influence radiation dose in temporal bone high-resolution multidetector row CT. Am J Neuroradiol 2011; 32:1783–1788.

17.

Meyer

Haubenreisser

Raupach

, et al. Initial results of a new generation dual source CT system using only an in-plane comb filter for ultra-high resolution temporal bone imaging. Eur Radiol 2015; 25:178–185.

18.

Cicchetti

DV.

Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology.

Psychol Assessment 1994; 6:284–290.

19.

Leng

Diehn

Lane

, et al. Temporal bone CT: Improved image quality and potential for decreased radiation dose using an ultra-high-resolution scan mode with an iterative reconstruction algorithm. Am J Neuroradiol 2015; 36:1599–1603.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.96 MB

1.47 MB

0.06 MB

Noise reduction and image quality in ultra-high resolution computed tomography of the temporal bone using advanced modeled iterative reconstruction